Finance data warehouse development

DICEUS helps financial institutions collect actionable insights and make informed decisions through data warehousing. This technology aggregates structured data from different sources, empowering organizations with real-time financial data analytics and reports. The banking data warehouse solutions we develop for our clients always align with industry regulations and data protection laws. 

Use cases of data warehousing in the financial industry 

Banks and other financial organizations manage a great amount of data in daily operations, which takes a lot of time and money. This is why building a financial data warehouse has become a reasonable solution to the problem. Firstly, it gives a clear understanding of an organization’s financial health, enabling reasonable business decisions about where to allocate resources. Secondly, data warehousing helps businesses track accurate financial indicators across numerous sources. Finally, the technology guarantees compliance with regulatory standards. 

A finance data warehouse is a centralized repository for storing data captured from disparate sources (finance, accounting, operations, and sales) and providing a consolidated view of information for decision-making.

The cases below demonstrate how you can use data warehouses for banks and other financial organizations.

Customer data management 

Data warehouses allow companies to gather and analyze personal information to enhance service delivery and strengthen customer relationships. Financial companies may learn what determines purchasing decisions and other consumer behaviors. Data warehouses allow you to quickly record and track large quantities of data effectively. 


Banking data warehouse solutions help with predictive and real-time analytics for financial businesses. They make it possible to store data in one place and access it when needed, helping to discover market trends and prepare for future events. Also, data warehouses are perfect for storing critical financial details like customer insights and scam detection details. 

Risk management 

The use of BDW allows you to derive powerful insights and make quick decisions to mitigate risks. It helps automate the risk management process to effectively track the threats created by different entities. The technology also protects data by not giving away complete information and enabling limited access to employees. 

Data workflow automation 

Banking DWH development includes the collection, extraction, and normalization of relevant data. It helps businesses build their automation using no-code tools. Using a central repository, they can fully control their processes and reduce the pressure on IT, which makes their operations more efficient. 

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Benefits of BDW

The historical evolution of data warehousing has resulted in challenges such as inefficient dataset utilization and inconsistent information. This decentralized approach also leads to a complex data governance process, making it difficult to ensure data accuracy and compliance. Furthermore, data silos, integration complexities, and the need for real-time data analysis pose additional challenges to the effective management of data in the finance industry. 

The benefits of a data warehouse for banking operations can help overcome the challenges mentioned above. 


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Our finance data warehouse development services 

DICEUS specializes in creating and maintaining analytical DWH for banks. We provide advisory, enrichment, and integration services based on our expertise in two distinctive approaches. 

Our first offering involves developing a data warehouse in financial services based on industrial banking data models from renowned brands like IBM, SAS Institute, and Oracle. 

Our second offer focuses on transforming existing data models within banks, tailoring them to meet specific requirements. With a wealth of experience, we ensure the customization of industrial models, providing banks with comprehensive solutions that include glossaries, conceptual models, logical models, and frequently used data marts. 

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How to get started 

The use of DWH in banking has become an absolute necessity. The team of DICEUS simplifies the process by facilitating seamless extraction and loading from various data sources to your data warehouse. Utilizing a variety of connectors and rapid data replication, you can efficiently transfer substantial datasets for tasks like migrations and advanced analytics. Feel free to embrace the advantages of data warehousing for banks and other financial institutions with us. If you have settled on implementing a banking data model, here are the steps to take for a successful project: 

Steps 1Determine the goals. Discover your business objectives (tactical and strategic) to outline the data warehouse scope and high-level system requirements. 
Step 2Develop a concept and select the platform. Determine the most convenient data warehouse solution and choose the optimal deployment approach. 
Step 3Create a business case and a project roadmap. Define data warehouse development project scope, budget planning, timeframes, and a deployment strategy.
Step 4Analyze the system and design the architecture. Conduct a detailed analysis of each data source (such as data type, data volume, data quality, data sensitivity, etc.), create data security policies, and design data models for analytical banking data warehouses. 
Step 5Develop and stabilize the solution. Proceed with data warehouse platform customization and performance testing, which might take up to two months. 
Step 6Launch the solution. Start data migration and data quality assessment before introducing the data warehouse for financial services. 
Step 7Secure after-launch support. Adjust data warehouse performance to users’ needs. 

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What is banking data warehousing?

A data warehousing system for financial services is a centralized repository of integrated financial data from different sources that is used to make more informed decisions.

What are the key components of BDW? 

A traditional DWH for banks has four main components: a central database, ETL tools, metadata, and access tools. All of them are engineered for speed so that you can get quick results and make a quick analysis.

What does data warehousing allow organizations to achieve? 

Enterprise data warehouse for financial services accumulates large amounts of data from numerous sources for further analysis. Its analytical capabilities help organizations receive practical business insights to optimize decision-making, streamline operations, and enhance general performance. 

What challenges are commonly faced in financial data warehouses?

A banking data warehouse model can face challenges related to data integration, as financial institutions deal with diverse data sources that need to be unified for comprehensive analysis. Maintaining data accuracy is another challenge, especially when dealing with large volumes of real-time and historical financial data. 

What is a financial data warehouse model? 

A financial DWH model is a structured framework for organizing and keeping financial data within a data warehouse. It includes data sources, data integration, data structure, granularity, security and access control, metadata management, scalability and performance. A well-developed model ensures a solid foundation for analysis and reporting.

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